Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network

Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network

Physical blending is one of the method to control and improve the mechanical properties of polymer such as Poly(lactic acid) or known as PLA. However, the phenomenological theory or model to connect the structure and properties of PLA blend is not available. Thus, in order to predict the mechanical...

Full description

Bibliographic Details
Published in:Evergreen
Main Author: Fatriansyah J.F.; Surip S.N.; Hartoyo F.
Format: Article
Language:English
Published: Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129101734&doi=10.5109%2f4774229&partnerID=40&md5=03f81f0a4641ac04ae03107a65106ba7
id 2-s2.0-85129101734
spelling 2-s2.0-85129101734
Fatriansyah J.F.; Surip S.N.; Hartoyo F.
Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network
2022
Evergreen
9
1
10.5109/4774229
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129101734&doi=10.5109%2f4774229&partnerID=40&md5=03f81f0a4641ac04ae03107a65106ba7
Physical blending is one of the method to control and improve the mechanical properties of polymer such as Poly(lactic acid) or known as PLA. However, the phenomenological theory or model to connect the structure and properties of PLA blend is not available. Thus, in order to predict the mechanical property from structure is based on many trial experiments. In this study, Deep Learning Network (DNN) was employed to predict the yield strength of PLA blend based on its structure information: blending composition, molecular weight, melting point and density of polymer. It was demonstrated that DNN can successfully predict the mechanical property from structure information of PLA blends although the accuracy could be further improved. © 2022 Novel Carbon Resource Sciences. All rights reserved.
Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy
21890420
English
Article
All Open Access; Gold Open Access
author Fatriansyah J.F.; Surip S.N.; Hartoyo F.
spellingShingle Fatriansyah J.F.; Surip S.N.; Hartoyo F.
Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network
author_facet Fatriansyah J.F.; Surip S.N.; Hartoyo F.
author_sort Fatriansyah J.F.; Surip S.N.; Hartoyo F.
title Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network
title_short Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network
title_full Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network
title_fullStr Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network
title_full_unstemmed Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network
title_sort Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network
publishDate 2022
container_title Evergreen
container_volume 9
container_issue 1
doi_str_mv 10.5109/4774229
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129101734&doi=10.5109%2f4774229&partnerID=40&md5=03f81f0a4641ac04ae03107a65106ba7
description Physical blending is one of the method to control and improve the mechanical properties of polymer such as Poly(lactic acid) or known as PLA. However, the phenomenological theory or model to connect the structure and properties of PLA blend is not available. Thus, in order to predict the mechanical property from structure is based on many trial experiments. In this study, Deep Learning Network (DNN) was employed to predict the yield strength of PLA blend based on its structure information: blending composition, molecular weight, melting point and density of polymer. It was demonstrated that DNN can successfully predict the mechanical property from structure information of PLA blends although the accuracy could be further improved. © 2022 Novel Carbon Resource Sciences. All rights reserved.
publisher Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy
issn 21890420
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
_version_ 1809677892752244736

Similar Items